Overcoming Barriers to Preventing and Treating P. aeruginosa Infections Using AAV Vectored Immunoprophylaxis
Bibliographic record
Abstract
Pseudomonas aeruginosa is a bacterial pathogen of global concern and is responsible for 10–15% of nosocomial infections worldwide. This opportunistic bacterial pathogen is known to cause serious complications in immunocompromised patients and is notably the leading cause of morbidity and mortality in patients suffering from cystic fibrosis. Currently, the only line of defense against P. aeruginosa infections is antibiotic treatment. Due to the acquired and adaptive resistance mechanisms of this pathogen, the prevalence of multidrug resistant P. aeruginosa strains has increased, presenting a major problem in healthcare settings. To date, there are no approved licensed vaccines to protect against P. aeruginosa infections, prompting the urgent need alternative treatment options. An alternative to traditional vaccines is vectored immunoprophylaxis (VIP), which utilizes a safe and effective adeno-associated virus (AAV) gene therapy vector to produce sustained levels of therapeutic monoclonal antibodies (mAbs) in vivo from a single intramuscular injection. In this review, we will provide an overview of P. aeruginosa biology and key mechanisms of pathogenesis, discuss current and emerging treatment strategies for P. aeruginosa infections and highlight AAV-VIP as a promising novel therapeutic platform.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".